By Dipanjan (DJ) Sarkar – 45 min read
Humans have an inherent ability to transfer knowledge across tasks. What we acquire as knowledge while learning about one task, we utilize in the same way to solve related tasks.
How to train Neural Network faster with optimizers?
By Piotr Skalski – 11 min read
As I worked on the last article, I had the opportunity to create my own neural network using only Numpy. It was a very challenging task, but at the same time it significantly broadened my understanding of the processes that take place inside the NN.
Whose fault is it when AI makes mistakes?
By Cassie Kozyrkov – 6 min read
Don’t get me wrong, I love machine learning and AI. But I don’t trust them blindly and neither should you, because the way you build effective and reliable ML/AI solutions is to force each solution to earn your trust.
A Bayesian Approach to Time Series Forecasting
By Daniel Foley – 19 min read
Today we are going to implement a Bayesian linear regression in R from scratch and use it to forecast US GDP growth. This post is based on a very informative manual from the Bank of England on Applied Bayesian Econometrics.
The xtensor vision
By Wolf Vollprecht – 7 min read
It is important to show a clear vision – especially for Open Source projects: only a common goal can bring people together. That is why we have decided to finally put together a document that lays out our vision on how C++ can play a major role in the future of data science, and why.
Tic Tac Toe – Creating Unbeatable AI with Minimax Algorithm
By Greg Surma – 6 min read
In today’s article, I am going to show you how to create an unbeatable AI agent that plays the classic Tic Tac Toe game. You will learn the concept of the Minimax algorithm that is widely and successfully used across the fields like Artificial Intelligence, Economics, Game Theory, Statistics or even Philosophy.
Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value
By William Koehrsen – 9 min read
These articles cover the concepts and a full implementation as applied to predicting customer churn. The project Jupyter Notebooks are all available on GitHub.
Predicting Professional Players’ Chess Moves with Deep Learning
By Sayon Bhattacharjee – 9 min read
So I’m bad at chess. My dad taught me when I was young, but I guess he was one of those dads who always let their kid win. To compensate for this lack of skill in one of the world’s most popular games, I did what any data science lover would do: build an AI to beat the people I couldn’t beat.
